Sir-Muguna/end-to-end-airflow-data-pipeline by Ronald MugunaSir-Muguna/end-to-end-airflow-data-pipeline by Ronald Muguna

Sir-Muguna/end-to-end-airflow-data-pipeline

Ronald Muguna

Data Visualizer

Data Analyst

Data Engineer

Google Cloud Platform

Microsoft Power BI

Python

Uber Data Pipeline and Analysis

This repository contains an end-to-end data pipeline for processing and analyzing Uber trip data. The pipeline ingests raw data, transforms it using Apache Airflow, and loads it into BigQuery for further analysis. The project also includes data modeling with dbt and an interactive dashboard for data visualization.

Dataset

The dataset used in this project describes yellow taxi trip data in New York City. You can download the dataset from the following link:

Dataset Download

For additional data dictionaries or a map of the TLC Taxi Zones, please visit the NYC TLC website.

Data Modeling

The data model for the project includes various fact and dimension tables created using dbt:

Prerequisites

To run this project, ensure you have the following installed:

Docker

Astro CLI

Google Cloud (GC) account

Setup Steps

Docker Configuration

Update Dockerfile: Ensure your Dockerfile uses the following base image:

quay.io/astronomer/astro-runtime:12.0.0

Restart Airflow using astro dev restart.

Dataset Preparation

Download the dataset:

Uber Data CSV

Store the CSV file in include/dataset/uber_data.csv.

Update requirements.txt:

Add apache-airflow-providers-google.

Restart Airflow.

Google Cloud Setup

Create a GCS bucket:

Name: <your_name>.

Create a service account:

Name: <your_name>.

Grant admin access to GCS and BigQuery.

Generate and save the service account key as include/gcp/service_account.json.

Set up Airflow connection:

Airflow → Admin → Connections.

ID: gcp.

Type: Google Cloud.

Keypath: /usr/local/airflow/include/gcp/service_account.json.

Test the connection.

Airflow DAG Creation

Create the DAG:

Test the task:

Create an empty Dataset: Test the connection:

Load file into BigQuery: Test the task:

🏆 Data loaded into the warehouse!

Build SQL dbt Models

Run dbt models:

# add the following to the Dockerfile
# install dbt into a virtual environment
RUN python -m venv dbt_venv && source dbt_venv/bin/activate && \
    pip install --no-cache-dir dbt-bigquery && deactivate

# add the following to the requirements.txt file
#Astro Runtime includes the following pre-installed providers packages: https://docs.astronomer.io/astro/runtime-image-architecture#provider-packages
astronomer-cosmos
astronomer-cosmos[dbt-bigquery]
protobuf
apache-airflow-providers-google

Add the SQL dbt models.

astro dev bash
source /usr/local/airflow/dbt_venv/bin/activate
cd include/dbt 
dbt deps
dbt run --profiles-dir /usr/local/airflow/include/dbt/
If there is an error after running dbt on google credentials, export the json key path,
export GOOGLE_APPLICATION_CREDENTIALS="/usr/local/airflow/include/gcp/service_account.json"

Verify that the tables exist with data in BigQuery.

Add dbt task:

Create DBT_CONFIG:

Test dbt task:

Run the pipeline:

Check the Airflow UI to verify the TaskGroup transform with the models.

Trigger the DAG to run the pipeline and create dbt model tables in BigQuery.

Dashboard

A simple interactive dashboard will be created on Looker for data analysis.

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Posted Jan 23, 2025

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